pyspark.RDD.saveAsHadoopDataset#

RDD.saveAsHadoopDataset(conf, keyConverter=None, valueConverter=None)[source]#

Output a Python RDD of key-value pairs (of form RDD[(K, V)]) to any Hadoop file system, using the old Hadoop OutputFormat API (mapred package). Keys/values are converted for output using either user specified converters or, by default, org.apache.spark.api.python.JavaToWritableConverter.

New in version 1.1.0.

Parameters

confdict

Hadoop job configuration

keyConverterstr, optional

fully qualified classname of key converter (None by default)

valueConverterstr, optional

fully qualified classname of value converter (None by default)

See also

SparkContext.hadoopRDD()

RDD.saveAsNewAPIHadoopDataset()

RDD.saveAsHadoopFile()

RDD.saveAsNewAPIHadoopFile()

RDD.saveAsSequenceFile()

Examples

>>> import os
>>> import tempfile

Set the related classes

>>> output_format_class = "org.apache.hadoop.mapred.TextOutputFormat"
>>> input_format_class = "org.apache.hadoop.mapred.TextInputFormat"
>>> key_class = "org.apache.hadoop.io.IntWritable"
>>> value_class = "org.apache.hadoop.io.Text"

>>> with tempfile.TemporaryDirectory(prefix="saveAsHadoopDataset") as d:
...     path = os.path.join(d, "old_hadoop_file")
...
...     # Create the conf for writing
...     write_conf = {
...         "mapred.output.format.class": output_format_class,
...         "mapreduce.job.output.key.class": key_class,
...         "mapreduce.job.output.value.class": value_class,
...         "mapreduce.output.fileoutputformat.outputdir": path,
...     }
...
...     # Write a temporary Hadoop file
...     rdd = sc.parallelize([(1, ""), (1, "a"), (3, "x")])
...     rdd.saveAsHadoopDataset(conf=write_conf)
...
...     # Create the conf for reading
...     read_conf = {"mapreduce.input.fileinputformat.inputdir": path}
...
...     # Load this Hadoop file as an RDD
...     loaded = sc.hadoopRDD(input_format_class, key_class, value_class, conf=read_conf)
...     sorted(loaded.collect())
[(0, '1\t'), (0, '1\ta'), (0, '3\tx')]